Back End Developer - AI Experience

Job Description

Role OverviewWe are looking for a Backend Developer with experience in Java, Quarkus, and AI development to join our team. AI will be a core part of your daily workflow, using tools such as GitLab Duo and Claude Code to accelerate coding, testing, and code reviews. You will help the team deliver backend tasks faster and with higher quality, bringing an AI-first mindset to how we build, test, and release backend services.ResponsibilitiesDesign, build and maintain backend services and APIs using Java and Quarkus.Use AI tools such as GitLab Duo and Claude Code as part of your daily workflow to accelerate coding, testing, debugging, and code review.Solve day-to-day backend team tasks – features, bug fixes, refactors, integrations – leveraging AI to move faster without compromising quality.Write automated tests (unit and integration) for backend services, using AI to speed up test creation and improve coverage.Participate in code reviews – both AI-assisted and with teammates – ensuring code quality and adherence to our engineering standards.Troubleshoot and resolve issues in CI/CD pipelines, using GitLab Duo to speed up root-cause analysis.Collaborate closely with other backend engineers, QA, and product to deliver reliable, well-tested software.Key Requirements4+ years of hands-on experience in backend development.Experience with Java. (Quarkus is a plus)Practical experience using AI-assisted development tools such as GitHub Copilot, GitLab Duo, Claude Code, or similar as part of a daily engineering workflow.Experience designing and building APIs and Java-based microservices that run reliably at scale in production.Experience writing automated unit and integration tests for backend systems.Ability to understand the functional and business context of a feature before designing the technical solution.Curiosity and willingness to experiment with new AI tools and integrate them responsibly into day-to-day development practices.Comfortable working in a fast-moving environment where AI-assisted development practices are still evolving.Strong attention to detail, critical thinking, and problem-solving skills.Good verbal and written communication skills in English.Collaborative, adaptable, and able to manage multiple priorities effectively.Nice to HaveExperience with LLM-based integrations, Retrieval-Augmented Generation (RAG), or agent orchestration workflows.Familiarity with MCP (Model Context Protocol) or similar tool/context integrations that connect AI assistants to internal systems.Exposure to AI-native development practices beyond code completion, such as spec-driven development frameworks, custom prompt or skill libraries, and automation workflows.MindsetPragmatic – knows when to move fast and when to invest in more solid foundations.Structured thinker – reasons about a problem before diving into code.Autonomous – takes ownership of tasks and knows when to ask for help.AI-enabled – uses modern AI tools to boost speed and quality, without losing engineering judgment.Collaborative – works well with other engineers, QA, and product, and enjoys building with context.